{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hart Algorithm Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pylab import rcParams\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "rcParams['figure.figsize'] = (13, 6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import nilmtk\n",
    "from nilmtk import DataSet, TimeFrame, MeterGroup, HDFDataStore\n",
    "from nilmtk.legacy.disaggregate import CombinatorialOptimisation\n",
    "from nilmtk.utils import print_dict\n",
    "from nilmtk.metrics import f1_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Data from HDF file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded 6 buildings\n"
     ]
    }
   ],
   "source": [
    "data = DataSet('/data/redd.h5')\n",
    "print('Loaded', len(data.buildings), 'buildings')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading data for Building 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "elec = data.buildings[1].elec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TimeFrame(start='2011-04-18 09:22:09-04:00', end='2011-05-24 15:57:02-04:00', empty=False)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elec.get_timeframe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set a window"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.set_window(start='2011-04-20',end='2011-04-24')\n",
    "elec_1 = data.buildings[1].elec"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mains and Submeters Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "mains=elec_1.mains()\n",
    "submeters=elec_1.submeters()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['apparent']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mains.available_ac_types('power')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['active']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submeters.available_ac_types('power')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Running HART_85"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nilmtk.legacy.disaggregate.hart_85 import Hart85\n",
    "h = Hart85()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Please set columns accordingly \n",
    "#### For REDD, since mains is 'Apparent Power'\n",
    "#### For IAWE, you may set columns to columns= [('power','active')] or to columns= [('power','active'),('power','reactive')]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading data for meter ElecMeterID(instance=2, building=1, dataset='REDD')     \n",
      "Done loading data all meters for this chunk.\n",
      "Finding Edges, please wait ...\n",
      "Edge detection complete.\n",
      "Creating transition frame ...\n",
      "Transition frame created.\n",
      "Creating states frame ...\n",
      "States frame created.\n",
      "Finished.\n"
     ]
    }
   ],
   "source": [
    "h.train(mains,columns=[('power','apparent')])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save Model using export_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#h.export_model('model.pickle')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import model after saving"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#h.import_model('model.pickle')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "Loading data for meter ElecMeterID(instance=1, building=1, dataset='REDD')     "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\nilmtk\\nilmtk\\metergroup.py:935: UserWarning: As a quick implementation we only get Good Sections from the first meter in the meter group.  We should really return the intersection of the good sections for all meters.  This will be fixed...\n",
      "  warnings.warn(\"As a quick implementation we only get Good Sections from\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading data for meter ElecMeterID(instance=2, building=1, dataset='REDD')     \n",
      "Done loading data all meters for this chunk.\n",
      "Finding Edges, please wait ...\n",
      "Edge detection complete.\n",
      "Creating transition frame ...\n",
      "Transition frame created.\n",
      "Creating states frame ...\n",
      "States frame created.\n",
      "Finished.\n",
      "Loading data for meter ElecMeterID(instance=2, building=1, dataset='REDD')     \n",
      "Done loading data all meters for this chunk.\n",
      "Finding Edges, please wait ...\n",
      "Edge detection complete.\n",
      "Creating transition frame ...\n",
      "Transition frame created.\n",
      "Creating states frame ...\n",
      "States frame created.\n",
      "Finished.\n",
      "Loading data for meter ElecMeterID(instance=2, building=1, dataset='REDD')     \n",
      "Done loading data all meters for this chunk.\n",
      "Finding Edges, please wait ...\n",
      "Edge detection complete.\n",
      "Creating transition frame ...\n",
      "Transition frame created.\n",
      "Creating states frame ...\n",
      "States frame created.\n",
      "Finished.\n",
      "Loading data for meter ElecMeterID(instance=2, building=1, dataset='REDD')     \n",
      "Done loading data all meters for this chunk.\n",
      "Finding Edges, please wait ...\n",
      "Edge detection complete.\n",
      "Creating transition frame ...\n",
      "Transition frame created.\n",
      "Creating states frame ...\n",
      "States frame created.\n",
      "Finished.\n",
      "Loading data for meter ElecMeterID(instance=2, building=1, dataset='REDD')     \n",
      "Done loading data all meters for this chunk.\n",
      "Finding Edges, please wait ...\n",
      "Edge detection complete.\n",
      "Creating transition frame ...\n",
      "Transition frame created.\n",
      "Creating states frame ...\n",
      "States frame created.\n",
      "Finished.\n",
      "Loading data for meter ElecMeterID(instance=2, building=1, dataset='REDD')     \n",
      "Done loading data all meters for this chunk.\n",
      "Finding Edges, please wait ...\n",
      "Edge detection complete.\n",
      "Creating transition frame ...\n",
      "Transition frame created.\n",
      "Creating states frame ...\n",
      "States frame created.\n",
      "Finished.\n",
      "Loading data for meter ElecMeterID(instance=2, building=1, dataset='REDD')     \n",
      "Done loading data all meters for this chunk.\n",
      "Next Chunk..\n",
      "Loading data for meter ElecMeterID(instance=2, building=1, dataset='REDD')     \n",
      "Done loading data all meters for this chunk.\n",
      "Next Chunk..\n",
      "Loading data for meter ElecMeterID(instance=2, building=1, dataset='REDD')     \n",
      "Done loading data all meters for this chunk.\n",
      "Next Chunk..\n",
      "Loading data for meter ElecMeterID(instance=2, building=1, dataset='REDD')     \n",
      "Done loading data all meters for this chunk.\n",
      "Next Chunk..\n",
      "Loading data for meter ElecMeterID(instance=2, building=1, dataset='REDD')     \n",
      "Done loading data all meters for this chunk.\n",
      "Next Chunk..\n",
      "Loading data for meter ElecMeterID(instance=2, building=1, dataset='REDD')     \n",
      "Done loading data all meters for this chunk.\n",
      "Next Chunk..\n",
      "Appending mains data to datastore\n",
      "Loading data for meter ElecMeterID(instance=2, building=1, dataset='REDD')     \n",
      "Done loading data all meters for this chunk.\n",
      "Done\n"
     ]
    }
   ],
   "source": [
    "output = HDFDataStore('output.h5', 'w')\n",
    "df=h.disaggregate(mains, output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Uncomment it for closing the Output HDFDataStore\n",
    "# output.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Returned Disaggregated Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-04-23 23:55:00-04:00</th>\n",
       "      <td>209</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-04-23 23:56:00-04:00</th>\n",
       "      <td>209</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-04-23 23:57:00-04:00</th>\n",
       "      <td>209</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-04-23 23:58:00-04:00</th>\n",
       "      <td>209</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-04-23 23:59:00-04:00</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             0  1  2\n",
       "2011-04-23 23:55:00-04:00  209  0  0\n",
       "2011-04-23 23:56:00-04:00  209  0  0\n",
       "2011-04-23 23:57:00-04:00  209  0  0\n",
       "2011-04-23 23:58:00-04:00  209  0  0\n",
       "2011-04-23 23:59:00-04:00    0  0  0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Since Hart is unsupervised, Find best matched appliances to disaggregated output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading data for meter ElecMeterID(instance=4, building=1, dataset='REDD')     \n",
      "Done loading data all meters for this chunk.\n",
      "Loading data for meter ElecMeterID(instance=20, building=1, dataset='REDD')     \n",
      "Done loading data all meters for this chunk.\n",
      "Best Matched Pair is (0, ElecMeterID(instance=5, building=1, dataset='REDD'))\n",
      "Best Matched Pair is (1, ElecMeterID(instance=6, building=1, dataset='REDD'))\n",
      "Best Matched Pair is (2, MeterGroupID(meters=(ElecMeterID(instance=10, building=1, dataset='REDD'), ElecMeterID(instance=20, building=1, dataset='REDD'))))\n"
     ]
    }
   ],
   "source": [
    "h.best_matched_appliance(submeters,df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## So it shows column 0's appliance best matches with Fridge, 1-> dish washer, 2-> washer dryer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MeterGroup(meters=\n",
       "  ElecMeter(instance=1, building=1, dataset='REDD', site_meter, appliances=[])\n",
       "  ElecMeter(instance=2, building=1, dataset='REDD', site_meter, appliances=[])\n",
       "  ElecMeter(instance=5, building=1, dataset='REDD', appliances=[Appliance(type='fridge', instance=1)])\n",
       "  ElecMeter(instance=6, building=1, dataset='REDD', appliances=[Appliance(type='dish washer', instance=1)])\n",
       "  ElecMeter(instance=7, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=1)])\n",
       "  ElecMeter(instance=8, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=2)])\n",
       "  ElecMeter(instance=9, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=1)])\n",
       "  ElecMeter(instance=11, building=1, dataset='REDD', appliances=[Appliance(type='microwave', instance=1)])\n",
       "  ElecMeter(instance=12, building=1, dataset='REDD', appliances=[Appliance(type='unknown', instance=1)])\n",
       "  ElecMeter(instance=13, building=1, dataset='REDD', appliances=[Appliance(type='electric space heater', instance=1)])\n",
       "  ElecMeter(instance=14, building=1, dataset='REDD', appliances=[Appliance(type='electric stove', instance=1)])\n",
       "  ElecMeter(instance=15, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=3)])\n",
       "  ElecMeter(instance=16, building=1, dataset='REDD', appliances=[Appliance(type='sockets', instance=4)])\n",
       "  ElecMeter(instance=17, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=2)])\n",
       "  ElecMeter(instance=18, building=1, dataset='REDD', appliances=[Appliance(type='light', instance=3)])\n",
       "  ElecMeter(instance=19, building=1, dataset='REDD', appliances=[Appliance(type='unknown', instance=2)])\n",
       "  MeterGroup(meters=\n",
       "    ElecMeter(instance=3, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])\n",
       "    ElecMeter(instance=4, building=1, dataset='REDD', appliances=[Appliance(type='electric oven', instance=1)])\n",
       "  )\n",
       "  MeterGroup(meters=\n",
       "    ElecMeter(instance=10, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
       "    ElecMeter(instance=20, building=1, dataset='REDD', appliances=[Appliance(type='washer dryer', instance=1)])\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elec  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparing for Fridge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\meira\\.conda\\envs\\nilmtk-env\\lib\\site-packages\\pandas\\core\\reshape\\merge.py:522: UserWarning: merging between different levels can give an unintended result (1 levels on the left, 2 on the right)\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    }
   ],
   "source": [
    "# First we need to take intersection of indices of dataframes of fridge and predicted (Inner Join)\n",
    "df_fridge = next(elec_1['fridge', 1].load())\n",
    "merged_df = pd.merge(df[0], df_fridge, left_index=True, right_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>(power, active)</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-04-22 22:49:00-04:00</th>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
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       "    <tr>\n",
       "      <th>2011-04-22 22:54:00-04:00</th>\n",
       "      <td>0</td>\n",
       "      <td>7.0</td>\n",
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       "    <tr>\n",
       "      <th>2011-04-22 22:55:00-04:00</th>\n",
       "      <td>0</td>\n",
       "      <td>7.0</td>\n",
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       "    <tr>\n",
       "      <th>2011-04-22 23:00:00-04:00</th>\n",
       "      <td>0</td>\n",
       "      <td>7.0</td>\n",
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       "    <tr>\n",
       "      <th>2011-04-22 23:01:00-04:00</th>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
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      ],
      "text/plain": [
       "                           0  (power, active)\n",
       "2011-04-22 22:49:00-04:00  0              6.0\n",
       "2011-04-22 22:54:00-04:00  0              7.0\n",
       "2011-04-22 22:55:00-04:00  0              7.0\n",
       "2011-04-22 23:00:00-04:00  0              7.0\n",
       "2011-04-22 23:01:00-04:00  0              6.0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "merged_df[0].plot(c='r')\n",
    "merged_df['power', 'active'].plot()\n",
    "plt.legend([\"Predicted\", \"Ground truth\"]);\n",
    "plt.ylabel(\"Power (W)\")\n",
    "plt.xlabel(\"Time\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparing for  Washer Dryer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading data for meter ElecMeterID(instance=20, building=1, dataset='REDD')     \n",
      "Done loading data all meters for this chunk.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\meira\\.conda\\envs\\nilmtk-env\\lib\\site-packages\\pandas\\core\\reshape\\merge.py:522: UserWarning: merging between different levels can give an unintended result (1 levels on the left, 2 on the right)\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    }
   ],
   "source": [
    "df_dish_washer = next(elec_1['washer dryer', 1].load())\n",
    "merged_df = pd.merge(df[2], df_fridge, left_index=True, right_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2</th>\n",
       "      <th>(power, active)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-04-22 22:49:00-04:00</th>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-04-22 22:54:00-04:00</th>\n",
       "      <td>0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-04-22 22:55:00-04:00</th>\n",
       "      <td>0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-04-22 23:00:00-04:00</th>\n",
       "      <td>0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-04-22 23:01:00-04:00</th>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           2  (power, active)\n",
       "2011-04-22 22:49:00-04:00  0              6.0\n",
       "2011-04-22 22:54:00-04:00  0              7.0\n",
       "2011-04-22 22:55:00-04:00  0              7.0\n",
       "2011-04-22 23:00:00-04:00  0              7.0\n",
       "2011-04-22 23:01:00-04:00  0              6.0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 936x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax1 = merged_df[2].plot(c='r')\n",
    "ax2 = merged_df['power', 'active'].plot(c='grey')\n",
    "ax1.legend([\"Predicted\", \"Ground truth\"])\n",
    "plt.ylabel(\"Power (W)\")\n",
    "plt.xlabel(\"Time\");"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
